Supply chain factoring repayment query method and device based on high concurrency and medium

Abstract

The invention relates to the technical field of data processing, and discloses a supply chain factoring repayment query method and device based on high concurrency and a medium, and the method comprises the steps: storing repayment information data corresponding to a factoring repayment page into a hash chain table; performing access frequency analysis on the repayment information data stored in the hash chain table, and when a batch repayment query request is received, splitting a query task corresponding to the batch repayment query request into a plurality of query sub-tasks; when all the query subtasks are successfully matched with the frequent query data in the cache, collecting repayment query results corresponding to all the query subtasks into a first query result data set; matching the query subtasks which are not successfully matched with the repayment information data in the hash chain table to obtain a second query result data set; and combining the first query result data set and the second query result data set into a factoring repayment query table. According to the invention, the efficiency of high-concurrency supply chain factoring repayment query can be improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a supply chain factoring repayment query method, device and medium based on high concurrency. Background Art

[0002] Factoring business is widely used in the commercial field, involving many financing institutions. Each institution may have multiple factoring contracts that require repayment inquiries. In addition, due to the increase in the customer base, the number of parallel visits to the factoring repayment inquiry page is also skyrocketing. However, in order to improve the efficiency of page queries, it is necessary to optimize the structure storage of the data to be queried in order to perform efficient factoring repayment queries.

[0003] The existing factoring repayment query technology uses concurrency control technology to manage concurrent access, thereby achieving high-concurrency factoring repayment query. In actual applications, in a concurrent environment, due to resource competition between multiple transactions, deadlock may occur, resulting in a slow query speed, which in turn reduces the efficiency of high-concurrency factoring repayment query. Summary of the invention

[0004] The present invention provides a method, device and medium for querying factoring repayments of supply chain based on high concurrency, the main purpose of which is to solve the problem of low efficiency when performing factoring repayment queries with high concurrency.

[0005] To achieve the above purpose, the present invention provides a supply chain factoring repayment query method based on high concurrency, comprising:

[0006] Storing the repayment information data corresponding to the factoring repayment page into a pre-generated hash table;

[0007] Performing access frequency analysis on the repayment information data stored in the hash linked list to obtain frequently queried data in the repayment information data, and storing the frequently queried data in a preset cache;

[0008] When receiving a batch repayment query request, splitting the query task corresponding to the batch repayment query request into multiple query subtasks;

[0009] Matching each query subtask with the frequently queried data in the cache;

[0010] When all query subtasks are successfully matched with the frequent query data in the cache, the repayment query results corresponding to all query subtasks are aggregated into a first query result data set;

[0011] When there is a query subtask that fails to match the frequent query data in the cache, the query subtask that fails to match is matched with the repayment information data in the hash linked list to obtain a second query result data set;

[0012] The first query result data set and the second query result data set are combined into a factoring repayment query table.

[0013] Optionally, before storing the repayment information data corresponding to the factoring repayment page in a pre-generated hash linked table, the method includes:

[0014] Extracting the repayment data type corresponding to the repayment information data;

[0015] Create a linked list node corresponding to each repayment data type;

[0016] Create a linked list header according to the factoring repayment identifier in the repayment data type;

[0017] Transversely connecting the linked list head with the linked list nodes one by one until all the linked list nodes corresponding to the repayment data type are connected;

[0018] When there are multiple chain heads, the chain heads corresponding to different factoring repayment identifiers are vertically connected until all chain heads are connected;

[0019] Generate a hash linked list based on the linked list nodes after horizontal connection and the linked list heads after vertical connection.

[0020] Optionally, storing the repayment information data corresponding to the factoring repayment page in a pre-generated hash linked table includes:

[0021] Calculate the hash value corresponding to each repayment data type in the repayment information data;

[0022] The hash values ​​corresponding to each factoring repayment identifier in the repayment data type are stored one by one in the header of the hash chain list;

[0023] Determine the data type identifier of each linked list node in the hash linked list according to the repayment data type;

[0024] According to the data type identifier, the hash value corresponding to each repayment data type is added to the linked list node of the hash linked list.

[0025] Optionally, performing access frequency analysis on the repayment information data stored in the hash linked list to obtain frequently queried data in the repayment information data includes:

[0026] Traversing the repayment information data in the hash linked table, and recording the query times corresponding to each repayment data type in the repayment information data after traversal;

[0027] Sort the repayment data types according to the query times in descending order to obtain a query times sequence corresponding to the repayment data types;

[0028] The repayment data types in the query frequency sequence that are greater than a preset query frequency threshold are regarded as frequent query data.

[0029] Optionally, splitting the query task corresponding to the batch repayment query request into multiple query subtasks includes:

[0030] Extracting the task type of the query task corresponding to the batch repayment query request;

[0031] Generate task subsets based on query tasks corresponding to the same task type;

[0032] Taking each of the task subsets as a query subtask;

[0033] The query subtasks are aggregated into a plurality of query subtasks.

[0034] Optionally, matching each query subtask with the frequently queried data in the cache includes:

[0035] Convert the repayment query data corresponding to each query subtask into the first word vector;

[0036] Convert the frequently searched data in the cache into the second word vector;

[0037] Calculate the matching degree between the first word vector and the second word vector;

[0038] It is determined whether each query subtask successfully matches the frequently queried data in the cache according to the matching degree.

[0039] Optionally, the step of aggregating the repayment query results corresponding to all query subtasks into a first query result data set includes:

[0040] Classifying the repayment query results according to the task type of the query subtask;

[0041] The repayment query results corresponding to the same task type after classification are collected into a repayment query dataset;

[0042] Deleting duplicate query results in the repayment query data set;

[0043] The deleted repayment query data set is aggregated into the first query result data set.

[0044] Optionally, merging the first query result data set and the second query result data set into a factoring repayment query table includes:

[0045] Filtering the same query results in the first query result data set and the second query result data set;

[0046] Filter different query results in the first query result data set and the second query result data set;

[0047] The same query results and the different query results are combined into a factoring repayment query table.

[0048] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0049] at least one processor; and,

[0050] a memory communicatively connected to the at least one processor; wherein,

[0051] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned high-concurrency supply chain factoring repayment query method.

[0052] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned high-concurrency supply chain factoring repayment query method.

[0053] The embodiment of the present invention can improve the security and reliability of data by storing repayment information data through a hash linked list, optimize the structure of the data to be queried, and after storage, analyze it with the big data record of the page, purposefully pre-cache the frequently queried data types to improve the efficiency of the query, and when receiving batch query requests at the same time, split and integrate the requests to achieve the diversion and synchronization of the request data, further improving the query efficiency. Therefore, the supply chain factoring repayment query method, device and medium based on high concurrency proposed by the present invention can solve the problem of low efficiency when performing high-concurrency factoring repayment queries. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of a flow chart of a method for querying supply chain factoring repayments based on high concurrency provided by an embodiment of the present invention;

[0055] Figure 2 A schematic diagram of a process for analyzing the access frequency of repayment information data provided by an embodiment of the present invention;

[0056] Figure 3A schematic diagram of a process for splitting a query task provided by an embodiment of the present invention;

[0057] Figure 4 A schematic diagram of the structure of an electronic device for implementing the high-concurrency supply chain factoring repayment query method provided in one embodiment of the present invention.

[0058] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0059] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0060] The embodiment of the present application provides a supply chain factoring repayment query method based on high concurrency. The execution subject of the supply chain factoring repayment query method based on high concurrency includes but is not limited to at least one of the electronic devices such as the server, the terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the supply chain factoring repayment query method based on high concurrency can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0061] Reference Figure 1 FIG. 1 is a flow chart of a method for querying supply chain factoring repayments based on high concurrency provided by an embodiment of the present invention. In this embodiment, the method for querying supply chain factoring repayments based on high concurrency includes:

[0062] S1. Store the repayment information data corresponding to the factoring repayment page into a pre-generated hash linked list.

[0063] In one of the practical application scenarios of the present invention, in supply chain finance, suppliers need to query the repayment status of their accounts receivable in a timely manner in order to carry out fund management and planning arrangements. The supply chain finance platform needs to provide financial reports and settlement information to relevant parties, including repayment records, account balances, etc., and the corresponding repayment information in the factoring repayment page needs to be stored in a pre-generated storage structure to meet the needs of a large number of suppliers to query the repayment status at the same time.

[0064] In the embodiment of the present invention, the hash list provides data non-tamperability and integrity guarantee. Once the data is stored in the hash list and the list is correctly maintained, no one can modify or tamper with the data without being detected. Therefore, it is necessary to construct a hash list to store the repayment information data corresponding to the factoring repayment page.

[0065] In the embodiment of the present invention, before storing the repayment information data corresponding to the factoring repayment page in a pre-generated hash linked table, the method includes:

[0066] Extracting the repayment data type corresponding to the repayment information data;

[0067] Create a linked list node corresponding to each repayment data type;

[0068] Create a linked list header according to the factoring repayment identifier in the repayment data type;

[0069] Transversely connecting the linked list head with the linked list nodes one by one until all the linked list nodes corresponding to the repayment data type are connected;

[0070] When there are multiple chain heads, the chain heads corresponding to different factoring repayment identifiers are vertically connected until all chain heads are connected;

[0071] Generate a hash linked list based on the linked list nodes after horizontal connection and the linked list heads after vertical connection.

[0072] In detail, the repayment information data contains information corresponding to various repayment data types, where the repayment data types include customer basic information, contract information, repayment plan, repayment record, overdue record, fee information, collateral information, approval process information, risk assessment information, and other additional information. The different data types in the repayment information data can be identified and extracted, and a corresponding linked list node is created for each repayment data type, such as creating a user basic information linked list node, a contract information linked list node, etc., and based on the factoring repayment identifier in the repayment data type, a corresponding linked list header is created. If a factoring repayment identifier exists, a factoring repayment linked list header is created.

[0073] Specifically, each linked list node is horizontally connected with the corresponding linked list head. For example, the factoring repayment identifier corresponding to customer A is horizontally connected with the linked list nodes corresponding to the customer's basic information, contract information, repayment plan, repayment record, overdue record, fee information, collateral information, approval process information, risk assessment information, and other additional information. Then, factoring repayment identifier → customer basic information → contract information → repayment plan → repayment record → overdue record → fee information → collateral information → batch process information → risk assessment information → other additional information is formed until all the linked list nodes corresponding to the repayment data types are connected; if there is Multiple linked list heads indicate that there is more than one customer, and there may be customer B, customer C, and so on. Then the factoring repayment identifiers corresponding to different customers are vertically connected, that is, the factoring repayment identifiers corresponding to different customers are arranged vertically, that is, factoring repayment identifier A→factoring repayment identifier B→factoring repayment identifier C, where different factoring repayment identifiers are arranged vertically, and then a hash linked list is generated according to the horizontally connected linked list nodes and the vertically connected linked list nodes, or a hash algorithm, such as SHA-256, is used to perform hash operations on the horizontally connected linked list nodes and the vertically connected linked list nodes to generate the final hash linked list.

[0074] Furthermore, through the hash linked list, the history of the data can be easily traced. Each data block contains the hash value of its previous data block, so it is possible to trace back to the initial data input point along the linked list to understand the complete change history of the data. In addition, the repayment data is stored on multiple nodes of the network instead of being stored in a single centralized database, thereby increasing the security and anti-attack ability of the data. Therefore, the repayment information data needs to be stored in the hash linked list.

[0075] In the embodiment of the present invention, storing the repayment information data corresponding to the factoring repayment page in a pre-generated hash linked table includes:

[0076] Calculate the hash value corresponding to each repayment data type in the repayment information data;

[0077] The hash values ​​corresponding to each factoring repayment identifier in the repayment data type are stored one by one in the header of the hash chain list;

[0078] Determine the data type identifier of each linked list node in the hash linked list according to the repayment data type;

[0079] According to the data type identifier, the hash value corresponding to each repayment data type is added to the linked list node of the hash linked list.

[0080] In detail, for each repayment data type, a hash algorithm (such as SHA-256) is used to calculate the hash value of its corresponding data. For each factoring repayment identifier, its corresponding hash value is stored one by one in the linked list header of the hash linked list. For example, if there are multiple factoring repayment identifiers corresponding to different customers, their hash values ​​are stored in different linked list header positions of the hash linked list respectively; and according to the repayment data type, the data type identifier corresponding to each linked list node in each hash linked list node is determined. For example, a field can be used to record the repayment data type to which each linked list node belongs. According to the data type identifier, the hash value corresponding to each repayment data type is added to the corresponding linked list node of the hash linked list. For example, the hash value of the customer's basic information is added to the customer's basic information linked list node, and the hash value of the contract information is added to the contract information linked list node, etc.

[0081] Furthermore, due to the increase in the customer base, the number of parallel visits to the factoring repayment query page is also skyrocketing. In order to improve the query efficiency of factoring repayment data, it is necessary to analyze the number of data accesses with the big data records of the page after storing the repayment information data.

[0082] S2. Perform access frequency analysis on the repayment information data stored in the hash linked list to obtain frequently queried data in the repayment information data, and store the frequently queried data in a preset cache.

[0083] In the embodiment of the present invention, the frequently queried data refers to repayment data items or repayment data types that are frequently queried in the data set.

[0084] In the embodiment of the present invention, referring to Figure 2 As shown, the access frequency analysis of the repayment information data stored in the hash linked list is performed to obtain the frequently queried data in the repayment information data, including:

[0085] S21, traversing the repayment information data in the hash linked table, and recording the query times corresponding to each repayment data type in the repayment information data after traversal;

[0086] S22, sorting the repayment data types according to the query times in descending order to obtain a query times sequence corresponding to the repayment data types;

[0087] S23: taking the repayment data types in the query frequency sequence that are greater than a preset query frequency threshold as frequent query data.

[0088] In detail, a dictionary or hash table can be used to record the query times of each repayment data type, with an initial value of 0. The hash table is traversed, and for each repayment data type, its query times are increased by 1, and then the dictionary recording the query times is sorted in descending order according to the query times, and the query times sequence is traversed. Then, for each repayment data type, if its query times are greater than the preset query times threshold, it is regarded as frequent query data, that is, repayment information data that is frequently queried.

[0089] Specifically, frequently queried data types are purposefully pre-cached to improve query efficiency, that is, first select a suitable caching method, such as Redis, Memcached, etc., configure the cache size according to memory resources and needs, determine the amount of data that the cache can store, and determine the cache replacement strategy, that is, when the cache is full, decide which data should be replaced. Common replacement strategies include but are not limited to LRU and LFU (Least Frequently Used). Then, frequently queried data can be stored in the cache, that is, traverse the frequently queried data list, and for each frequently queried data item, store it in the cache. The data item can be used as the cache key, and the corresponding value can be the data itself or other identifiers. When other parts need to access frequently queried data, first check whether the data exists in the cache. If it exists, directly obtain it from the cache, otherwise obtain it from the storage system and store it in the cache, and the cache needs to be updated regularly or when the data changes to keep the data in the cache synchronized with the data in the storage system.

[0090] Furthermore, the factoring repayment page involves many types of data that need to be queried and written off. When a large number of users make inquiries at the same time, it often causes response jams or even page crashes, resulting in low query efficiency. Therefore, when receiving a large number of query requests, it is necessary to analyze the request method of the query request to improve data query efficiency.

[0091] S3. When a batch repayment query request is received, the query task corresponding to the batch repayment query request is split into multiple query subtasks.

[0092] In one of the practical application scenarios of the present invention, the supply chain finance platform usually involves multiple participants, including suppliers, buyers and financial institutions. When a supplier needs to inquire about the repayment status of its factoring (or similar financial products), a large number of concurrent repayment query requests may appear, especially when the repayment date is approaching or the contract expires, there may be sudden query pressure. In this case, in order to effectively handle a large number of query requests, it is necessary to concurrently split the query tasks corresponding to the repayment query requests into multiple query subtasks for batch repayment query requests, so that batch repayment query requests can be processed efficiently.

[0093] In the embodiment of the present invention, the query subtask refers to splitting the query task corresponding to the batch repayment query request into smaller and more specific task units so that these tasks can be processed and executed more efficiently. If the batch query request includes two task types, namely, querying the outstanding amount and querying the repayment history, the query subtask can be a separate query for each account, and the outstanding amount query and repayment history query of each account can be split into independent query subtasks. Therefore, the query subtask is a smaller, independently executable unit in the batch query task.

[0094] In the embodiment of the present invention, referring to Figure 3 As shown, the query task corresponding to the batch repayment query request is split into multiple query subtasks, including:

[0095] S31, extracting the task type of the query task corresponding to the batch repayment query request;

[0096] S32, generating a task subset according to query tasks corresponding to the same task type;

[0097] S33, taking each of the task subsets as a query subtask;

[0098] S34: Aggregate the query subtasks into multiple query subtasks.

[0099] In detail, the task type is extracted from the batch repayment query request, such as querying arrears, querying repayment history, contract information, etc., and query tasks with the same task type are gathered together to form a task subset, so as to ensure that each query subtask concentrates on processing queries of the same type, improving processing efficiency, and each task subset is regarded as a query subtask, which contains a group of query tasks with the same task type, and finally the formed query subtasks are gathered again to form multiple query subtasks. This can better utilize system resources and improve concurrent processing capabilities. In addition, when batch query requests are received at the same time, the requests are split and integrated to achieve the diversion and synchronization of the request data, further improving the query efficiency.

[0100] For example, assuming that there is a batch repayment query request including two task types: querying arrears and querying repayment history, the query request is first divided into two subsets, one containing tasks for querying arrears and the other containing tasks for querying repayment history; then, each subset is taken as a query subtask; finally, the two query subtasks are combined again according to resource conditions to form the final multiple query subtasks so as to execute the query tasks concurrently.

[0101] Furthermore, when performing data query, it is first necessary to query whether the repayment data exists in the cache, that is, to first check whether there is a copy of the required data in the cache. Therefore, each query subtask needs to be matched and analyzed with the data in the cache.

[0102] S4. Match each query subtask with the frequently queried data in the cache.

[0103] In the embodiment of the present invention, by matching with the frequently queried data in the cache, repeated execution of the same or similar queries can be avoided, thereby reducing the number of accesses to the database and improving the repayment data query efficiency.

[0104] In the embodiment of the present invention, matching each query subtask with the frequently queried data in the cache includes:

[0105] Convert the repayment query data corresponding to each query subtask into the first word vector;

[0106] Convert the frequently searched data in the cache into the second word vector;

[0107] Calculate the matching degree between the first word vector and the second word vector;

[0108] It is determined whether each query subtask successfully matches the frequently queried data in the cache according to the matching degree.

[0109] In detail, preprocessing operations are performed on the repayment query data in each query subtask, such as word segmentation, removal of stop words, part-of-speech tagging, etc., to facilitate subsequent vectorization operations, and then the processed query subtask is converted into a vector representation through a vector conversion model (such as Word2Vec, Bert model) to obtain the vector representation of the query subtask, that is, the first word vector; similarly, the processed frequent query data is converted into a vector representation through a vector conversion model (such as Word2Vec, Bert model) to obtain the vector representation of the frequent query data in the cache, that is, the second word vector.

[0110] Specifically, the matching degree between the first word vector and the second word vector is calculated by a similarity algorithm, wherein the similarity algorithm includes but is not limited to cosine similarity and Euclidean distance, and then the calculated matching degree is compared with a preset threshold to determine whether the query subtask is considered to be successfully matched with the cached data. The matching degree of each cached data and the query subtask is compared. If there is cached data whose similarity exceeds the threshold, it is considered that the query subtask is successfully matched with the cached data. If the matching degree between the first word vector and the second word vector is 100%, it is considered that the repayment data of the query corresponding to the query subtask exists in the cache.

[0111] Furthermore, by determining whether each query subtask successfully matches the frequently queried data in the cache, the number of accesses to the database can be reduced and the data query efficiency can be improved.

[0112] S5. When all query subtasks are successfully matched with the frequent query data in the cache, the repayment query results corresponding to all query subtasks are aggregated into a first query result data set.

[0113] In an embodiment of the present invention, when all query subtasks are successfully matched with the frequently queried data in the cache, it means that the repayment data that each query subtask needs to query are frequently queried data, and the repayment data can be queried in the cache, thereby improving the query efficiency of the repayment data.

[0114] In an embodiment of the present invention, the first query result data set refers to a data set obtained by classifying and aggregating the repayment query results corresponding to all query subtasks according to their types and query times, that is, a set in which the repayment query results are organized together according to the query task type and time. The first query result data set may include multiple categories, each category represents a query subtask type, and each category contains multiple subsets, each subset represents the results obtained by querying within a certain time range.

[0115] In the embodiment of the present invention, the step of aggregating the repayment query results corresponding to all query subtasks into a first query result data set includes:

[0116] Classifying the repayment query results according to the task type of the query subtask;

[0117] The repayment query results corresponding to the same task type after classification are collected into a repayment query dataset;

[0118] Deleting duplicate query results in the repayment query data set;

[0119] The deleted repayment query data set is aggregated into the first query result data set.

[0120] In detail, the repayment query results are classified according to the task type of the query subtask, for example, all the repayment query results are classified according to the categories of bill query, repayment record query, loan balance query, etc., so as to form different repayment query result categories; for each query task type, the corresponding query results are aggregated into a data set, and in each data set, duplicate query results are deleted to ensure that each query result is unique, and then each data set after classification and deduplication processing is aggregated into a large data set, namely the first query result data set, which contains the repayment query results corresponding to all query subtasks.

[0121] Furthermore, when all query subtasks successfully match the frequently queried data in the cache, it means that each query subtask can query the repayment data in the cache, but not all repayment data can be queried in the cache. When some repayment data is not found in the cache, it is necessary to query in the hash linked list that pre-stores the repayment data to improve the accuracy of data query.

[0122] S6. When there is a query subtask that fails to match the frequent query data in the cache, the query subtask that fails to match is matched with the repayment information data in the hash linked list to obtain a second query result data set.

[0123] In an embodiment of the present invention, when there is a query subtask that fails to successfully match the frequent query data in the cache, that is, there is a query subtask that fails to successfully match the frequent query data in the cache, for each query subtask that fails to successfully match, relevant repayment information data is retrieved from the hash list, and for each query subtask that fails to successfully match, the corresponding repayment query result is obtained by comparing and matching with the repayment information data in the hash list, and the successfully matched repayment information data is used to form a second query result data set. The second query result data set includes the repayment query result corresponding to the query subtask that successfully matched in the cache and the repayment information data in the hash list. The repayment query result is obtained after successfully matching.

[0124] In detail, the matching of the unsuccessfully matched query subtasks with the repayment information data in the hash list is consistent with the step of matching each query subtask with the frequent query data in the cache in step S4, and will not be repeated here.

[0125] Specifically, when all query subtasks fail to match the frequent query data in the cache, all query subtasks need to be matched with the repayment information data in the hash list, so as to obtain the repayment query result after successfully matching the repayment information data in the hash list; and when the query subtask fails to match the frequent query data in the cache and fails to match the repayment information data in the hash list, it will be prompted that the repayment data query corresponding to this query subtask is incorrect, or the customer will be reminded to enter the correct query conditions to obtain the correct repayment query result.

[0126] Furthermore, a query result data set is obtained after a successful match with the frequent query data in the cache, and a query result data set is also obtained after a successful match with the repayment information data in the hash list. The two query result data sets need to be merged to obtain the query result corresponding to the complete repayment data.

[0127] S7. Combine the first query result data set and the second query result data set into a factoring repayment query table.

[0128] In the embodiment of the present invention, the factoring repayment query table refers to a data table containing the repayment results after querying the relevant repayment information in the factoring business, which can provide a clear repayment query result when receiving batch query requests at the same time.

[0129] In the embodiment of the present invention, the step of merging the first query result data set and the second query result data set into a factoring repayment query table includes:

[0130] Filtering the same query results in the first query result data set and the second query result data set;

[0131] Filter different query results in the first query result data set and the second query result data set;

[0132] The same query results and the different query results are combined into a factoring repayment query table.

[0133] In detail, the same query results are screened out from the first query result data set and the second query result data set, and the query results are query results that appear in both data sets; different query results are screened out from the first query result data set and the second query result data set, and the query results are query results that appear only in one data set but not in the other data set; and then the screened out same query results and different query results are merged into a factoring repayment query table, and the factoring repayment query table contains all query results in the two data sets, ensuring that each query result appears only once, and obtaining a factoring repayment query table that merges the first query result and the second query result, which includes all information of the same query results and the different query results.

[0134] The embodiment of the present invention can improve the security and reliability of data by storing repayment information data through a hash linked list, optimize the structure of the data to be queried, and after storage, analyze it with the big data record of the page, purposefully pre-cache the frequently queried data types to improve the efficiency of the query, and when receiving batch query requests at the same time, split and integrate the requests to achieve the diversion and synchronization of the request data, further improving the query efficiency. Therefore, the supply chain factoring repayment query method, device and medium based on high concurrency proposed by the present invention can solve the problem of low efficiency when performing high-concurrency factoring repayment queries.

[0135] like Figure 4 , is a schematic diagram of the structure of an electronic device for implementing a supply chain factoring repayment query method based on high concurrency provided by an embodiment of the present invention.

[0136] The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a supply chain factoring repayment inquiry program based on high concurrency.

[0137] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, and executes or executes the programs or modules stored in the memory 11 (for example, executing a supply chain factoring repayment inquiry program based on high concurrency, etc.), and calls the data stored in the memory 11 to execute various functions of the electronic device and process data.

[0138] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 can also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 can also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of a supply chain factoring repayment inquiry program based on high concurrency, but also can be used to temporarily store data that has been output or is to be output.

[0139] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0140] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0141] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0142] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0143] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0144] The high-concurrency supply chain factoring repayment query program stored in the memory 11 in the electronic device is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0145] Storing the repayment information data corresponding to the factoring repayment page into a pre-generated hash table;

[0146] Performing access frequency analysis on the repayment information data stored in the hash linked list to obtain frequently queried data in the repayment information data, and storing the frequently queried data in a preset cache;

[0147] When receiving a batch repayment query request, splitting the query task corresponding to the batch repayment query request into multiple query subtasks;

[0148] Matching each query subtask with the frequently queried data in the cache;

[0149] When all query subtasks are successfully matched with the frequent query data in the cache, the repayment query results corresponding to all query subtasks are aggregated into a first query result data set;

[0150] When there is a query subtask that fails to match the frequent query data in the cache, the query subtask that fails to match is matched with the repayment information data in the hash linked list to obtain a second query result data set;

[0151] The first query result data set and the second query result data set are combined into a factoring repayment query table.

[0152] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0153] Furthermore, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0154] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0155] Storing the repayment information data corresponding to the factoring repayment page into a pre-generated hash table;

[0156] Performing access frequency analysis on the repayment information data stored in the hash linked list to obtain frequently queried data in the repayment information data, and storing the frequently queried data in a preset cache;

[0157] When receiving a batch repayment query request, splitting the query task corresponding to the batch repayment query request into multiple query subtasks;

[0158] Matching each query subtask with the frequently queried data in the cache;

[0159] When all query subtasks are successfully matched with the frequent query data in the cache, the repayment query results corresponding to all query subtasks are aggregated into a first query result data set;

[0160] When there is a query subtask that fails to match the frequent query data in the cache, the query subtask that fails to match is matched with the repayment information data in the hash linked list to obtain a second query result data set;

[0161] The first query result data set and the second query result data set are combined into a factoring repayment query table.

[0162] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, media and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0163] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0164] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0165] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0166] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is not limited only according to the above description, and it is intended that all changes within the meaning and scope of equivalent elements within the scope of protection are included in the present invention.

[0167] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0168] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A supply chain factoring repayment query method based on high concurrency, characterized in that: The method comprises: Storing the repayment information data corresponding to the factoring repayment page into a pre-generated hash table; Performing access frequency analysis on the repayment information data stored in the hash linked list to obtain frequently queried data in the repayment information data, and storing the frequently queried data in a preset cache; When receiving a batch repayment query request, splitting the query task corresponding to the batch repayment query request into multiple query subtasks; Matching each query subtask with the frequently queried data in the cache; When all query subtasks are successfully matched with the frequent query data in the cache, the repayment query results corresponding to all query subtasks are aggregated into a first query result data set; When there is a query subtask that fails to match the frequent query data in the cache, the query subtask that fails to match is matched with the repayment information data in the hash linked list to obtain a second query result data set; The first query result data set and the second query result data set are combined into a factoring repayment query table.

2. The high-concurrency supply chain factoring repayment query method according to claim 1, characterized in that: Before storing the repayment information data corresponding to the factoring repayment page in a pre-generated hash chain table, the method includes: Extracting the repayment data type corresponding to the repayment information data; Create a linked list node corresponding to each repayment data type; Create a linked list header according to the factoring repayment identifier in the repayment data type; Transversely connecting the linked list head with the linked list nodes one by one until all the linked list nodes corresponding to the repayment data type are connected; When there are multiple chain heads, the chain heads corresponding to different factoring repayment identifiers are vertically connected until all chain heads are connected; Generate a hash linked list based on the linked list nodes after horizontal connection and the linked list heads after vertical connection.

3. The high-concurrency supply chain factoring repayment query method according to claim 1, characterized in that: The step of storing the repayment information data corresponding to the factoring repayment page into a pre-generated hash chain table includes: Calculate the hash value corresponding to each repayment data type in the repayment information data; The hash values ​​corresponding to each factoring repayment identifier in the repayment data type are stored one by one in the header of the hash chain list; Determine the data type identifier of each linked list node in the hash linked list according to the repayment data type; According to the data type identifier, the hash value corresponding to each repayment data type is added to the linked list node of the hash linked list.

4. The high-concurrency supply chain factoring repayment query method according to claim 1, characterized in that: The access frequency analysis of the repayment information data stored in the hash linked list to obtain the frequently queried data in the repayment information data includes: Traversing the repayment information data in the hash linked table, and recording the query times corresponding to each repayment data type in the repayment information data after traversal; Sort the repayment data types according to the query times in descending order to obtain a query times sequence corresponding to the repayment data types; The repayment data types in the query frequency sequence that are greater than a preset query frequency threshold are regarded as frequent query data.

5. The high-concurrency supply chain factoring repayment query method according to claim 1, characterized in that: The step of splitting the query task corresponding to the batch repayment query request into a plurality of query subtasks includes: Extracting the task type of the query task corresponding to the batch repayment query request; Generate task subsets based on query tasks corresponding to the same task type; Taking each of the task subsets as a query subtask; The query subtasks are aggregated into a plurality of query subtasks.

6. The high-concurrency supply chain factoring repayment query method according to claim 1, characterized in that: The matching of each query subtask with the frequently queried data in the cache includes: Convert the repayment query data corresponding to each query subtask into the first word vector; Convert the frequently searched data in the cache into the second word vector; Calculate the matching degree between the first word vector and the second word vector; It is determined whether each query subtask successfully matches the frequently queried data in the cache according to the matching degree.

7. The high-concurrency supply chain factoring repayment query method according to claim 1, characterized in that: The repayment query results corresponding to all query subtasks are collected into a first query result data set, including: Classifying the repayment query results according to the task type of the query subtask; The repayment query results corresponding to the same task type after classification are collected into a repayment query dataset; Deleting duplicate query results in the repayment query data set; The deleted repayment query data set is aggregated into the first query result data set.

8. The high-concurrency supply chain factoring repayment query method according to claim 1, characterized in that: The step of combining the first query result data set and the second query result data set into a factoring repayment query table includes: Filtering the same query results in the first query result data set and the second query result data set; Filter different query results in the first query result data set and the second query result data set; The same query results and the different query results are combined into a factoring repayment query table.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the high-concurrency supply chain factoring repayment query method as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements the high-concurrency supply chain factoring repayment query method as described in any one of claims 1 to 8.

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